THE POPULATION FREQUENCIES OF SPECIES AND THE ESTIMATION OF POPULATION PARAMETERS

By Irving John Good. 1953

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Citations in the corpus

A bit of progress in language modeling (2001, methodology): “5 Katz smoothing (Katz, 1987) is based on the Good-Turing formula (Good, 1953).”
An Empirical Study of Smoothing Techniques for Language Modeling (1996, methodology): “We can motivate absolute discounting using the Good-Turing estimate. Church and Gale (1991) show empirically that the average Good-Turing discount (r r ) associated with n-grams with larger counts (r 3) is largely constant over r. Further supporting evidence is presented in Section 5.2.1. Furthermor”
Word Association Norms, Mutual Information, and Lexicography (1989, methodology): “But to justify this statement, we need to compensate the window size (which shifts the score downward by 2.0, e.g. from 0.96 down to - 1.04) and we need to estimate the standard deviation, using a method such as Good (1953).)”
Entropy-based Pruning of Backoff Language Models (2000, methodology): “We used Good-Turing smoothing [3] throughout and did not investigate possible interactions between smoothing methods and pruning.”
Measuring temporal change in alpha diversity: A framework integrating taxonomic, phylogenetic and functional diversity and the iNEXT.3D standardization (2021, methodology): “This is a concept originally developed by Alan Turing and I. J. Good in their cryptographic analysis during World War II (Good, 1953).”
ESTIMATION OF SPECIES RICHNESS: MIXTURE MODELS, THE ROLE OF RARE SPECIES, AND INFERENTIAL CHALLENGES (2005, background): “For example, statistician I. J. Good, one of the first to tackle this problem (Good 1953), later stated: ‘‘I don’t believe it is usually possible to estimate the number of species . . . but only an appropriate lower bound to that number.”
Estimating diversity and entropy profiles via discovery rates of new species (2015, background): “By applying slope estimators derived from an improved Good–Turing frequency formula (Chao & Jost 2012), we obtain a more accurate continuous diversity and entropy profile.”
Stimulating the In Situ Activity of Geobacter Species To Remove Uranium from the Groundwater of a Uranium-Contaminated Aquifer (2003, background): “Coverage for all libraries was greater than 86% ( 14 ).”
Synchrony and seasonality in bacterioplankton communities of two temperate rivers (2005, methodology): “The two clone libraries were compared statistically using the LIBSHUFF computer program (Singleton et al. 2001), which calculates coverage (Good 1953) at various levels of evolutionary distance determined using the Jukes–Cantor model for nucleic acid substitution.”
Crowdsourced enumeration queries (2013, methodology): “pN is unknown, the Good-Turing estimator [19] using the f -statistic is used:”
Novel and Unexpected Prokaryotic Diversity in Water and Sediments of the Alkaline, Hypersaline Lakes of the Wadi An Natrun, Egypt (2007, methodology): “Diversity. Coverage was calculated using the formula C =1 _ (n1/N), where n1 is the number of OTUs with one representative and N is the total number of clones analyzed [ 14 ].”
Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. (2012, methodology): “If we consider all possible combinations ofsampled individuals, the expected value of the coverage of a sample of size m is given by (Good 1953, Robbins 1968) as follows: 1=1 = i - ~ p>îm m>0- ( 2 ) i=i For our Community A,the expected coverage for m = 20 is 57%, so the expected coverage deficit is…”